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Hardware-algorithm co-design in analog reservoir computing with nonlinearity of solution-processed 2D materials.

Songwei Liu1, Yingyi Wen1, Jingfang Pei1

  • 1Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong SAR, China.

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Summary
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This study introduces hardware-algorithm co-design for analog reservoir computing using 2D materials. This approach enables efficient chaotic system analysis and robust generalization for applications like IoTs.

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Area of Science:

  • Materials Science
  • Computer Science
  • Physics

Background:

  • Reservoir computing (RC) is a recurrent neural network approach effective for chaotic dynamics analysis.
  • Digital implementation of RC faces computational challenges due to iterative nonlinear mapping.
  • Analog systems using physical nonlinear materials offer a promising alternative for reservoir activation.

Purpose of the Study:

  • To investigate hardware-algorithm co-design for analog reservoir computing using solution-processed 2D materials.
  • To demonstrate the feasibility of analog nonlinear mapping for chaotic system regression.
  • To explore the potential of 2D materials in scalable analog computing.

Main Methods:

  • Utilized nonlinearity from solution-processed 2D materials for analog reservoir activation.
  • Developed a hardware-algorithm co-design framework for optimizing device-parameterized models.
  • Implemented reservoir computing models for chaotic system regression and synchronization tasks.

Main Results:

  • Successfully fitted 2D material nonlinearity as analog activation functions.
  • Achieved long-term synchronization and robust generalization in chaotic system regression.
  • Demonstrated resilience to noise in the analog reservoir computing model.
  • Showcased the scalability and lightweight potential of 2D material-based systems.

Conclusions:

  • Hardware-algorithm co-design with 2D materials is effective for analog reservoir computing.
  • This approach overcomes limitations of digital reservoir computing for chaotic dynamics.
  • The developed scheme holds significant potential for scalable, low-power analog computing in IoTs, wearables, and robotics.